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GEN1797 Master AI Code Governance Before It Masters You

$199.00
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The Executive Diagnostic and Governance Toolkit

Master AI Code Governance Before It Masters You

Score your own function red, amber or green, find out which part is weakest, and walk into the next budget round able to defend what you want to fix. Built for leaders reviewing aI-written code is becoming widespread enough to require its own governance layer. This means AI-generated code is no longer experimental, it is being deployed at scale, creating new risks in security, compliance, and maintainability. Developers and compliance officers who do not understand AI governance will lose influence. The first wave of accountability frameworks for AI code will emerge within 12 months. The immediate question: Request a demonstration from your DevOps team on how AI-generated code is currently tracked and audited.

$199 one-time
30-day money-back guarantee Verified against latest insights, updated access provided within 24h

Each order is checked and updated against the latest insights before delivery. That is why access takes up to 24 hours rather than being instant.

What you walk out with
A scored, ranked picture of your own function, and a defensible answer to what to fix first.
1 You stop guessing where you stand.
You finish with a score, not an opinion: every part of your function rated red, amber or green, with the weakest ranked first. Evidence: a Quick Scan for the shape of it, then seven domain assessments of 30 scored questions each, 210 in all, rolled into one scorecard, plus a maturity radar and a current-versus-target gap analysis.
2 You can defend the decision.
You walk into the budget round with the gap named, the owner named and done defined, instead of a case built on instinct. Evidence: project charter, scope statement, RACI, requirements traceability and work breakdown structure, pre-filled in your domain's language.
3 The work actually moves.
The month after the decision is already built, so nothing stalls waiting for someone to design a form. Evidence: more than 60 project templates across all five PMBOK process groups, plus runbooks, SOPs, a KPI framework, audit checklists and a risk matrix. 55 to 65 files in total.
4 You use it the day it lands.
No blank templates to interpret. Every workbook opens with what it is, who uses it, when, how, a 1 to 5 scoring guide, what good looks like, and a worked example you delete and type over.
The Quick Scan is one sitting. You will know your weakest area before the day is out.
Nothing in it is generic project management: the build rejects any file that could belong to another course. Updated after you enrol, so it reflects where the work stands now. The 144-chapter course is included behind it, for the parts you want to go deeper on.
Your team is deploying AI-written code. But no one can tell you where it’s used, how it’s reviewed, or whether it’s auditable.

The situation this is built for

AI-generated code is no longer experimental. It’s embedded in production systems, bypassing traditional code review, security scanning, and compliance checks. Developers move fast, but governance lags. The result: undocumented dependencies, unapproved libraries, and code that can’t be maintained. You’re expected to ensure integrity, but lack visibility into AI-authored changes. Without a governance layer specific to AI-written code, your audits will fail, your risk posture weakens, and your influence erodes. The first accountability frameworks are emerging. If you don’t define the standards, someone else will.

Who this is for

IT leaders, operations managers, compliance officers, and service management leads responsible for code integrity, audit readiness, and system governance in organizations where AI tools are used to generate production code.

Who this is not for

Developers looking to build AI coding tools, executives seeking vendor comparisons, or teams without AI-generated code already in deployment or CI/CD pipelines.

What you walk away with

  • Map where AI-generated code is deployed in production systems
  • Establish audit trails specific to AI-authored code changes
  • Define ownership and review requirements for AI-written functions
  • Enforce compliance controls for training data and model lineage
  • Implement versioning and rollback procedures for AI-generated modules

How this maps to your situation

  • Untracked AI code entering production
  • Lack of audit readiness for AI-generated systems
  • Compliance gaps in automated code generation
  • Erosion of control due to decentralized AI tool use

Before vs. after

Before
AI-generated code moves through your systems without oversight, creating blind spots in security, compliance, and operations.
After
You have a documented governance framework, audit-ready traceability, and enforced controls for all AI-written code in production.

What's included with your purchase

  • 12 modules with 12 chapters each (144 chapters)
  • Downloadable templates and worked examples for every module
  • Hand-built implementation playbook delivered alongside course access
  • 30-day money-back guarantee

Delivery and format

  • Course and learning environment access provisioned within 24 hours of purchase
  • Hand-built implementation playbook delivered alongside course access

Format: Text-based modules and chapters in the Art of Service learning environment, plus downloadable templates and worked examples for every chapter, plus the hand-built implementation playbook delivered alongside course access.

Time investment: Approximately 3 hours per module, designed for completion in 90 days with biweekly implementation sprints.

If nothing changes
Without governance, AI-generated code will create undetected security flaws, compliance violations, and technical debt that undermines system integrity and erodes stakeholder trust.

How this compares to the alternatives

Unlike generic AI ethics courses or developer-focused toolkits, this program focuses exclusively on the governance artefacts, decision points, and control mechanisms required by IT, compliance, and service leaders responsible for production code integrity.

Also included: the full course, for when you want the reasoning behind a finding (12 modules, 144 chapters)

Depth reference. The diagnostic and the templates stand on their own; this is what to read when you want the reasoning behind a finding.

Module 1. Recognize AI-Generated Code in Your Environment
Identify where AI-written code is already in use across repositories, pipelines, and production systems.
12 chapters in this module
  1. Define what constitutes AI-generated code in your organization
  2. Map AI tool usage across development teams and pipelines
  3. Identify codebases with untracked AI-authored contributions
  4. Establish baseline detection methods for AI-written functions
  5. Differentiate between assisted and fully AI-generated code
  6. Document instances of AI-generated code in production
  7. Assess accuracy of version control annotations for AI code
  8. Evaluate logs for AI tool invocation patterns
  9. Determine scope of undocumented AI code usage
  10. Classify risk levels based on AI code deployment context
  11. Identify teams bypassing formal AI governance channels
  12. Create inventory of AI-generated components by system
Module 2. Establish Governance Boundaries for AI Code
Define where human oversight is mandatory and where AI-authored code requires formal approval.
12 chapters in this module
  1. Define criticality thresholds for AI-generated functions
  2. Map regulatory obligations to AI code deployment areas
  3. Determine which systems prohibit autonomous AI code changes
  4. Establish human-in-the-loop requirements for code generation
  5. Classify code modules by maintainability and AI risk
  6. Set boundaries for AI use in security-critical components
  7. Document exceptions to AI code governance policies
  8. Define approval workflows for AI-generated pull requests
  9. Identify integration points requiring dual sign-off
  10. Align AI governance scope with existing compliance frameworks
  11. Determine ownership for AI code in shared repositories
  12. Create policy exceptions log for emergency AI deployments
Module 3. Audit AI Code for Security and Compliance
Implement checks that detect vulnerabilities and policy violations specific to AI-generated code.
12 chapters in this module
  1. Adapt static analysis tools for AI-written code patterns
  2. Identify common security flaws in AI-generated functions
  3. Audit for hardcoded credentials introduced by AI tools
  4. Verify license compliance of AI-suggested dependencies
  5. Detect use of deprecated or vulnerable libraries in AI output
  6. Review AI code for adherence to data handling policies
  7. Assess cryptographic implementation in AI-authored modules
  8. Evaluate AI-generated code for regulatory alignment
  9. Document findings from AI code security assessments
  10. Integrate AI-specific rules into existing SAST pipelines
  11. Flag AI code that bypasses input validation standards
  12. Measure compliance drift in AI-generated pull requests
Module 4. Implement Traceability for AI-Authored Changes
Ensure every AI-written line of code can be traced to its origin, author, and approval path.
12 chapters in this module
  1. Require metadata tagging for all AI-generated code
  2. Define mandatory fields in AI code commit messages
  3. Implement automated logging of AI tool usage events
  4. Integrate AI source tracking into version control
  5. Verify traceability of AI code in deployment manifests
  6. Map AI-generated functions to incident response records
  7. Enforce audit trail completeness before merge approval
  8. Link AI code versions to model version identifiers
  9. Track AI tool prompts alongside generated output
  10. Validate traceability during compliance audits
  11. Audit logs for gaps in AI change documentation
  12. Enforce traceability in rollback and patch procedures
Module 5. Define Ownership and Accountability Models
Assign clear responsibility for AI-generated code throughout its lifecycle.
12 chapters in this module
  1. Determine primary owner for AI-written production modules
  2. Define accountability for AI code maintenance and updates
  3. Assign code review responsibilities for AI-generated pull requests
  4. Clarify incident response ownership for AI-authored failures
  5. Document escalation paths for AI code-related outages
  6. Establish SLAs for patching AI-generated vulnerabilities
  7. Define custody handoffs between developers and operations
  8. Map AI code ownership to service catalog entries
  9. Require sign-off from owners before AI code promotion
  10. Audit ownership assignments during compliance reviews
  11. Track ownership changes in configuration management database
  12. Enforce ownership validation in deployment gates
Module 6. Govern Training Data and Model Lineage
Ensure the data used to train AI coding tools meets compliance and intellectual property standards.
12 chapters in this module
  1. Document sources of training data for AI coding tools
  2. Verify training data compliance with privacy regulations
  3. Audit training data for copyrighted or proprietary content
  4. Map AI model versions to specific code generation outcomes
  5. Assess risk of data leakage through AI suggestions
  6. Establish approval process for new training data ingestion
  7. Track data provenance in AI model development lifecycle
  8. Define retention policies for training data artifacts
  9. Evaluate model card completeness for deployed AI tools
  10. Require data lineage documentation for AI-generated libraries
  11. Assess bias risks in AI-suggested code patterns
  12. Document data governance exceptions for AI models
Module 7. Enforce Code Review and Approval Workflows
Adapt code review processes to address risks unique to AI-generated code.
12 chapters in this module
  1. Define mandatory review criteria for AI-written functions
  2. Implement dual-review requirements for high-risk AI code
  3. Adapt pull request templates for AI-generated submissions
  4. Train reviewers to spot AI-specific anti-patterns
  5. Verify human understanding of AI-generated logic
  6. Assess code quality metrics specific to AI output
  7. Evaluate maintainability of AI-written modules
  8. Require explanation of AI code logic in review notes
  9. Enforce comment-to-code ratio in AI-generated submissions
  10. Audit review completeness for AI pull requests
  11. Track reviewer performance on AI code assessments
  12. Update coding standards to address AI-generated patterns
Module 8. Manage Dependencies and Third-Party Risk
Control the inclusion of external libraries and services suggested by AI coding tools.
12 chapters in this module
  1. Audit AI-suggested dependencies for license compliance
  2. Block prohibited packages in AI-generated code output
  3. Evaluate security posture of third-party libraries recommended by AI
  4. Maintain approved list of dependencies for AI tool use
  5. Assess supply chain risks in AI-proposed integrations
  6. Require manual approval for new dependency introductions
  7. Monitor for deprecated or unmaintained packages in AI code
  8. Enforce dependency pinning in AI-generated scripts
  9. Track transitive dependencies in AI-suggested libraries
  10. Assess compatibility of AI-recommended frameworks
  11. Document rationale for accepting high-risk dependencies
  12. Integrate dependency checks into AI-assisted development
Module 9. Ensure Maintainability and Knowledge Transfer
Prevent AI-generated code from becoming unmaintainable technical debt.
12 chapters in this module
  1. Require inline documentation for AI-written functions
  2. Enforce commenting standards in AI-generated code
  3. Verify developer understanding before merging AI code
  4. Assess long-term maintainability of AI-authored modules
  5. Create knowledge transfer requirements for AI code owners
  6. Document assumptions made by AI in generated logic
  7. Evaluate test coverage for AI-written components
  8. Require unit tests alongside AI-generated implementations
  9. Track technical debt accumulation in AI codebases
  10. Define retirement criteria for AI-generated modules
  11. Audit code readability in AI-authored submissions
  12. Establish refactoring cycles for legacy AI code
Module 10. Integrate AI Governance into CI/CD Pipelines
Embed governance checks directly into automated build and deployment workflows.
12 chapters in this module
  1. Insert AI code detection at pull request initiation
  2. Enforce metadata tagging in pre-commit hooks
  3. Integrate AI governance checks into CI gateways
  4. Block deployments lacking AI code traceability
  5. Validate AI code reviews before merge approval
  6. Scan for policy violations in AI-generated output
  7. Require AI model version declaration in build logs
  8. Enforce dependency compliance in automated pipelines
  9. Trigger alerts for unapproved AI tool usage
  10. Log AI governance decisions in deployment records
  11. Measure AI governance compliance in release metrics
  12. Audit pipeline effectiveness for AI code control
Module 11. Prepare for Audits and Regulatory Scrutiny
Produce evidence that AI-generated code meets internal and external compliance requirements.
12 chapters in this module
  1. Assemble documentation package for AI code audits
  2. Verify completeness of AI code traceability records
  3. Prepare model lineage reports for compliance reviewers
  4. Demonstrate adherence to data governance policies
  5. Conduct mock audits of AI-generated code repositories
  6. Document exceptions to AI governance policies
  7. Show proof of human oversight in AI code approval
  8. Present training data provenance to auditors
  9. Verify retention of AI tool interaction logs
  10. Produce compliance dashboard for AI code metrics
  11. Respond to auditor inquiries about AI code quality
  12. Update policies based on audit findings
Module 12. Scale Governance Across Hybrid Development Teams
Extend AI code governance consistently across distributed, multi-vendor, and outsourced teams.
12 chapters in this module
  1. Align AI governance standards across vendor teams
  2. Enforce consistent policies in outsourced code delivery
  3. Train external developers on AI code requirements
  4. Audit third-party AI code submissions for compliance
  5. Standardize AI code documentation across geographies
  6. Integrate contractors into AI governance workflows
  7. Monitor AI tool usage in partner environments
  8. Enforce contract terms related to AI code quality
  9. Assess AI governance maturity in acquisition targets
  10. Extend governance automation to external pipelines
  11. Measure compliance consistency across teams
  12. Update governance framework based on cross-team feedback

Frequently asked

Who is this course for?
IT leaders, compliance officers, operations managers, and service governance leads responsible for code quality, audit readiness, and system integrity where AI tools are used to generate production code.
How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Does the course cover specific AI coding tools?
No. The course focuses on governance practices, not tool-specific features or integrations.
Will I receive templates I can use immediately?
Yes. Each module includes downloadable templates for policies, audit checklists, and implementation plans.
Is there a technical prerequisite?
Familiarity with code repositories, CI/CD pipelines, and compliance audits is expected, but no coding is required.
What formats do the templates come in?
The implementation playbook downloads as PDF and editable XLSX. The course reads in your learning environment and exports to PDF for offline use. The files are yours to keep.
Can I share this with my team?
The licence is per person. Team pricing opens from three seats: reply to the order confirmation with TEAM and we will set it up.
How quickly can I start?
The diagnostic is one sitting and the templates work straight out of the kit. Account access takes up to 24 hours rather than being instant, because every order is checked and updated against the latest sources before it is delivered.
$199 one-time. Approximately 3 hours per module, designed for completion in 90 days with biweekly implementation sprints..

Within 24 hours your account in the learning environment is provisioned and the tailored implementation playbook is delivered alongside it.

30-day money-back guarantee·Know your weakest area today·210 scored questions·Course included· Account access within 24 hours
30-day money-back guarantee, no questions asked.
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